To ensure that machine learning models are transparent, fair, and reliable, data scientists can use TrustyAI in {productname-short} to monitor and assess their data science models.
Data scientists can monitor their data science and machine learning models in {productname-short} for the following metrics:
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Bias: Check for unfair patterns or biases in data and model predictions to ensure your model’s decisions are unbiased.
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Data drift: Detect changes in input data distributions over time by comparing the latest real-world data to the original training data. Comparing the data identifies shifts or deviations that could impact model performance, ensuring that the model remains accurate and reliable.
Data scientists can assess their data science and machine learning models in {productname-short} using the following services:
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LLM evaluation: Monitor your Large Language Models (LLMs) against a range of metrics, in order to ensure the accuracy and quality of its output.
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Guardrails Orchestrator: Invoke detections on text generation inputs and outputs of Large Language Models (LLMs) and perform standalone detections.